WO2021057917A1 - Procédé et appareil d'estimation de température de batterie, dispositif électronique et support de stockage - Google Patents

Procédé et appareil d'estimation de température de batterie, dispositif électronique et support de stockage Download PDF

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Publication number
WO2021057917A1
WO2021057917A1 PCT/CN2020/117853 CN2020117853W WO2021057917A1 WO 2021057917 A1 WO2021057917 A1 WO 2021057917A1 CN 2020117853 W CN2020117853 W CN 2020117853W WO 2021057917 A1 WO2021057917 A1 WO 2021057917A1
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Prior art keywords
battery
temperature
functional relationship
admittance
target
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PCT/CN2020/117853
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English (en)
Chinese (zh)
Inventor
李晓倩
冯天宇
刘思佳
尹永刚
舒时伟
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比亚迪股份有限公司
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Application filed by 比亚迪股份有限公司 filed Critical 比亚迪股份有限公司
Priority to JP2022519442A priority Critical patent/JP7379679B2/ja
Priority to EP20870190.4A priority patent/EP4024069A4/fr
Priority to US17/764,340 priority patent/US11846675B2/en
Priority to KR1020227011623A priority patent/KR20220057614A/ko
Publication of WO2021057917A1 publication Critical patent/WO2021057917A1/fr

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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K7/00—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements
    • G01K7/16—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements using resistive elements
    • G01K7/26—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements using resistive elements the element being an electrolyte
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K7/00—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements
    • G01K7/42—Circuits effecting compensation of thermal inertia; Circuits for predicting the stationary value of a temperature
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/005—Circuits for comparing several input signals and for indicating the result of this comparison, e.g. equal, different, greater, smaller (comparing phase or frequency of 2 mutually independent oscillations in demodulators)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present disclosure relates to the field of battery technology, and in particular, to a battery temperature estimation method, device, electronic equipment, and storage medium.
  • the internal temperature of the power battery of a new energy vehicle is estimated in real time by collecting current, voltage and battery surface temperature, or by obtaining the relationship between the characteristic quantity of electrochemical impedance spectroscopy and the ambient temperature in a steady state and combining the measured impedance value To estimate the internal temperature of the power battery of a new energy vehicle.
  • these methods do not consider the impact of the battery's real-time charging and discharging state on the measured impedance value, and the internal temperature obtained is actually the average temperature of the battery as a whole, not the actual temperature inside the battery.
  • the accuracy of the internal temperature estimation of the battery is not accurate. high.
  • the present disclosure proposes a battery temperature estimation method, device, electronic equipment and storage medium, which can realize the estimation of the internal temperature of the battery in combination with the admittance and surface temperature measured in real time of the battery, and effectively improve the accuracy of the estimation of the internal temperature of the battery.
  • an embodiment of the present disclosure proposes a battery temperature estimation method.
  • the method includes: when the battery is offline, fitting a first functional relationship according to the corresponding admittance of the battery at different test temperatures; and according to the battery
  • the shape and size of the battery are used to obtain the temperature distribution model of the battery, and the second functional relationship corresponding to the internal temperature and the surface temperature is determined in combination with the first functional relationship.
  • the second functional relationship is used to combine the surface temperature of the battery and the battery admittance.
  • the internal temperature of the battery is estimated.
  • the battery temperature estimation method proposed in the embodiments of the present disclosure fits the first functional relationship according to the corresponding admittance of the battery at different test temperatures when the battery is offline, and obtains the temperature distribution model of the battery according to the shape and size of the battery ,
  • the second functional relationship corresponding to the internal temperature and the surface temperature is determined in combination with the first functional relationship.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can be combined with the real-time measured conductance of the battery.
  • Nano and surface temperature estimate the internal temperature of the battery, effectively improving the accuracy of the internal temperature estimation of the battery.
  • an embodiment of the present disclosure proposes a battery temperature estimation device, the device includes: a fitting module for fitting a first function according to the admittance corresponding to the battery at different test temperatures when the battery is offline Relationship; a first determination module for obtaining a temperature distribution model of the battery according to the shape and size of the battery, and determining a second functional relationship corresponding to the internal temperature and the surface temperature in combination with the first functional relationship, the second The functional relationship is used to estimate the internal temperature of the battery in combination with the surface temperature of the battery and the battery admittance.
  • the battery temperature estimation device proposed in the embodiment of the present disclosure fits the first functional relationship according to the admittance corresponding to the battery at different test temperatures when the battery is offline, and obtains the temperature distribution model of the battery according to the shape and size of the battery ,
  • the second functional relationship corresponding to the internal temperature and the surface temperature is determined in combination with the first functional relationship.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can be combined with the real-time measured conductance of the battery.
  • Nano and surface temperature estimate the internal temperature of the battery, effectively improving the accuracy of the internal temperature estimation of the battery.
  • an embodiment of the present disclosure proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • the processor implements the embodiment of the first aspect of the present disclosure when the program is executed.
  • the proposed battery temperature estimation method is a method for estimating battery temperature of the first aspect of the present disclosure.
  • the electronic device proposed in the embodiments of the present disclosure fits the first functional relationship according to the admittance corresponding to the battery at different test temperatures when the battery is offline, and obtains the temperature distribution model of the battery according to the shape and size of the battery.
  • the first functional relationship determines the second functional relationship corresponding to the internal temperature and the surface temperature.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can realize the real-time measured admittance and
  • the surface temperature estimates the internal temperature of the battery, effectively improving the accuracy of the internal temperature estimation of the battery.
  • an embodiment of the present disclosure proposes a computer-readable storage medium on which a computer program is stored, where the program is implemented when executed by a processor: the battery temperature estimation method proposed by the embodiment of the first aspect of the present disclosure.
  • the computer-readable storage medium proposed in the embodiments of the present disclosure fits the first functional relationship according to the admittance corresponding to the battery at different test temperatures when the battery is offline, and obtains the temperature distribution of the battery according to the shape and size of the battery Model, combined with the first functional relationship to determine the second functional relationship corresponding to the internal temperature and the surface temperature.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can realize the real-time measurement in combination with the battery Admittance and surface temperature estimate the internal temperature of the battery, effectively improving the accuracy of the internal temperature estimation of the battery.
  • FIG. 1 is a schematic flowchart of a method for estimating battery temperature according to an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of a curve of the first function relationship in an embodiment of the disclosure
  • FIG. 3 is a schematic diagram of discrete square batteries in an embodiment of the disclosure.
  • FIG. 4 is a schematic flowchart of a method for estimating battery temperature according to another embodiment of the present disclosure.
  • FIG. 5 is a schematic diagram of an EIS curve in an embodiment of the disclosure.
  • FIG. 6 is a schematic diagram of a response curve of admittance with temperature in a 50% charged state in an embodiment of the disclosure
  • FIG. 7 is a schematic flowchart of a method for estimating battery temperature according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of a temperature change curve during a battery charging process in an embodiment of the disclosure.
  • FIG. 9 is a schematic structural diagram of a battery temperature estimation device provided by an embodiment of the present disclosure.
  • FIG. 10 is a schematic structural diagram of a battery temperature estimation device provided by another embodiment of the present disclosure.
  • FIG. 11 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • the embodiments of the present disclosure provide a battery temperature estimation method, which fits the first functional relationship according to the corresponding admittance of the battery at different test temperatures when the battery is offline, and according to the battery shape and The size obtains the temperature distribution model of the battery, and determines the second function relationship between the internal temperature and the surface temperature in combination with the first function relationship.
  • the second function relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance.
  • FIG. 1 is a schematic flowchart of a battery temperature estimation method proposed by an embodiment of the present disclosure.
  • the method includes:
  • the offline state refers to the state that the battery is currently in the test environment, or it can also be the state that the battery is not charged and discharged, and there is no limitation on this.
  • the first functional relationship is fitted according to the corresponding admittance of the battery at different test temperatures.
  • the real part of the admittance corresponding to the battery at different test temperatures can be determined, and the first functional relationship can be fitted according to the different test temperatures and the real part of the corresponding admittance, where the first functional relationship is used for Describe the correspondence between different test temperatures and the real part of the admittance corresponding to the battery, introduce the concept of admittance to simplify the calculation formula, reduce the amount of calculation, and reduce the demand for chip computing power.
  • the first functional relationship can be fitted according to different test temperatures, corresponding admittance real parts, and the Arrhenius formula.
  • S102 Obtain the temperature distribution model of the battery according to the shape and size of the battery, and determine the second functional relationship corresponding to the internal temperature and the surface temperature in combination with the first functional relationship.
  • the second functional relationship is used to combine the surface temperature of the battery and the battery admittance to the battery.
  • the internal temperature is estimated.
  • the first functional relationship can be integrated, and According to the temperature distribution model, the second functional relationship of the battery is determined in combination with the first functional relationship after the integral processing.
  • the temperature distribution model is used to describe the corresponding relationship between the temperature at different locations inside the battery, the maximum temperature inside the battery, and the surface temperature of the battery.
  • the functional relationship between the temperature T(x) at different locations and the maximum temperature inside the battery and the surface temperature of the battery can be determined according to the one-dimensional steady-state heat conduction equation and used as a temperature distribution model, for example, Among them, T max represents the highest temperature inside the battery, T 0 represents the surface temperature of the battery, x represents different positions inside the battery, h is the thickness of the battery, ⁇ is the current heat flow of the battery, and ⁇ is the thermal conductivity of the battery.
  • the shape of the battery may be, for example, a regular or irregular shape, and the shape of the battery may be, for example, a hexahedral shape.
  • the shape of the battery is a square as an example, and there is no limitation on this.
  • a square battery can be regarded as composed of multiple battery slices discrete in the direction of the battery thickness.
  • the multiple battery slices are, for example, N battery slices.
  • N is a positive integer greater than or equal to 2
  • the The battery is discrete into N cells in the thickness direction.
  • FIG. 3 is a schematic diagram of the discretization of a square battery in an embodiment of the disclosure.
  • the square battery is dispersed into N cells in the thickness direction, and each cell may correspond to the x coordinate.
  • One corresponding position on the axis and multiple corresponding positions can correspond to x in the above-mentioned temperature distribution model T(x).
  • the first functional relationship can also be integrated Processing and determining the second functional relationship of the battery according to the temperature distribution model and the first functional relationship after the integral processing.
  • the real part of the admittance measured by the first functional relationship in the above steps should be the superposition of the real part of the admittance of the battery as a whole.
  • T k represents the temperature corresponding to the k-th battery slice
  • the specific value of T k is related to the position of the battery slice inside the battery
  • h is the thickness value of the battery
  • x represents different positions inside the battery .
  • the first functional relationship is fitted according to the corresponding admittance of the battery at different test temperatures, and the temperature distribution model of the battery is obtained according to the shape and size of the battery, combined with the first functional relationship Determine the second functional relationship corresponding to the internal temperature and the surface temperature.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can be combined with the real-time measured admittance and surface temperature of the battery.
  • the internal temperature is estimated, which effectively improves the accuracy of the internal temperature estimation of the battery.
  • FIG. 4 is a schematic flowchart of a method for estimating battery temperature according to another embodiment of the present disclosure.
  • the method includes:
  • the characteristic test frequency belongs to the target frequency range. In the target frequency range, the impedance of the battery does not change with the change in the state of charge of the battery.
  • the characteristic test frequency is obtained by pre-testing, and the characteristic test frequency is a frequency in the target frequency range. In the target frequency range, the impedance of the battery does not change with the change of the state of charge of the battery.
  • the characteristic test frequency is used for testing The battery undergoes an in-situ admittance test.
  • the electrochemical impedance spectroscopy test in the process of determining the characteristic test frequency, can be performed on the battery in combination with the preset frequency range under different charged states, and according to the test result, from the preset frequency range Determine the target frequency range and test the response value of the electrochemical impedance spectrum of the battery under different temperatures in combination with the set state of charge, and convert the response value into the corresponding admittance.
  • the actual admittance in the target frequency range The frequency with the largest change in the part with temperature is determined as the characteristic test frequency.
  • the in-situ admittance test of the battery based on the characteristic test frequency can effectively assist in the establishment of the standard relationship between the in-situ admittance test data and the ambient temperature data. Small estimation error, improve estimation accuracy.
  • the feature test frequency can be determined in any other possible way.
  • traditional programming techniques such as simulation and engineering methods
  • it can also be inherited. Learn algorithms and artificial neural networks to determine the frequency of feature testing.
  • the preset frequency range may be preset by actual test experience, and the preset frequency range may be 0.01 Hz-1 kHz.
  • the battery is subjected to electrochemical impedance spectroscopy test in combination with a preset frequency range under different charged states.
  • the target frequency range is determined from the preset frequency range, for example, at room temperature (25 °C) Adjust the state-of-charge SOC to 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, respectively, with a square battery with a capacity of 50Ah at a rate of 0.5C. After 90% and 100%, perform Electrochemical Impedance Spectroscopy (EIS) test.
  • the preset frequency range is 0.01Hz-1kHz.
  • the target frequency range is 100 Hz-1 kHz, see FIG. 5, which is a schematic diagram of an EIS curve in an embodiment of the disclosure.
  • the response value of the electrochemical impedance spectrum of the test battery is combined with the set state of charge, and the response value is converted into the corresponding admittance.
  • the target frequency range the real part of the admittance varies with The frequency with the largest temperature change is determined as the characteristic test frequency.
  • the square battery is placed at temperatures of -20°C, -10°C, 0°C, 10°C, and 20°C, respectively.
  • test the response value of electrochemical impedance spectroscopy at different temperatures convert the response value of impedance to the corresponding admittance, and determine the conductivity in the target frequency range 100Hz-1kHz determined above.
  • S402 Determine the real part of the admittance corresponding to the battery at different test temperatures according to the characteristic test frequency.
  • the battery is tested in situ in an environment with a temperature of -20°C, -10°C, 0°C, 10°C, 20°C, 30°C, and 40°C under the state of charge and discharge.
  • the admittance of extract the real admittance part G', and then trigger step S403.
  • S404 Obtain the temperature distribution model of the battery according to the shape and size of the battery, and determine the second functional relationship corresponding to the internal temperature and the surface temperature in combination with the first functional relationship.
  • the second functional relationship is used to combine the surface temperature of the battery and the battery admittance to the battery.
  • the internal temperature is estimated.
  • the real part of the admittance measured by the first functional relationship in the above steps should be the superposition of the real part of the admittance of the battery as a whole.
  • T k represents the temperature corresponding to the k-th battery slice
  • the specific value of T k is related to the position of the battery slice inside the battery
  • h is the thickness value of the battery
  • T(x) f(T max ,T 0 ,x)
  • T(x) at different positions is a function of the maximum internal temperature and the surface temperature of the battery, where T max represents the maximum internal temperature of the battery, and T 0 represents The surface temperature of the battery
  • x represents different positions inside the battery .
  • the in-situ admittance test of the battery based on the characteristic test frequency can effectively assist in establishing the standard relationship between the in-situ admittance test data and the ambient temperature data, reduce estimation errors, and improve estimation accuracy.
  • the first function relationship is fitted according to the corresponding admittance of the battery at different test temperatures, and the temperature distribution model of the battery is obtained according to the shape and size of the battery, and the internal temperature and the surface are determined by combining the first function relationship The second functional relationship corresponding to the temperature.
  • the second functional relationship is used to estimate the internal temperature of the battery by combining the surface temperature of the battery and the battery admittance, and it can be used to estimate the internal temperature of the battery by combining the admittance and surface temperature measured in real time by the battery. Effectively improve the accuracy of battery internal temperature estimation.
  • FIG. 7 is a schematic flowchart of a battery temperature estimation method proposed by an embodiment of the present disclosure.
  • the method includes:
  • S701 Detect the target surface temperature of the battery to be estimated, and detect the target admittance of the battery to be estimated.
  • a temperature sensor can be configured on the battery to be estimated in advance to detect the target surface temperature of the battery to be estimated in real time, and to detect the target admittance of the battery to be estimated in real time.
  • S702 Determine the internal temperature of the battery to be estimated according to the target surface temperature and the target admittance in combination with the second functional relationship.
  • the substituted functional relationship is regarded as the second functional relationship
  • T max represents the maximum temperature inside the battery
  • T 0 represents the battery surface temperature.
  • the target surface temperature and the target admittance can be combined with the target surface temperature and the target admittance can be substituted into the second functional relationship to determine T max , and use the calculated T max as the actual temperature inside the battery, thereby realizing the estimation of the maximum temperature inside the battery.
  • FIG. 8 is a schematic diagram of the temperature change curve during the charging process of the battery in the embodiment of the present disclosure.
  • the time curves respectively represent the estimated battery internal temperature T est , the overall equivalent battery temperature T eq , the actual tested battery internal temperature T i and the battery surface temperature T 0 in the embodiments of the present disclosure, compared to the battery surface temperature and the battery overall, etc. Effective temperature, the internal temperature of the battery estimated in the embodiment of the present disclosure is closer to the measured internal temperature of the battery.
  • the internal temperature of the battery to be estimated is determined according to the target surface temperature and the target admittance in combination with the second functional relationship, taking into account the internal temperature of the battery to be estimated Temperature distribution, which can accurately give the highest internal temperature, used to estimate the internal temperature of the actual vehicle battery, so that the battery management system can further optimize the battery working state according to the internal temperature, ensure that the battery works in a safe temperature range, and solve the hidden dangers of flammability and explosion , Improve the safety and reliability of battery operation.
  • FIG. 9 is a schematic structural diagram of a battery temperature estimation device provided by an embodiment of the present disclosure.
  • the device 900 includes:
  • the fitting module 901 is configured to fit the first functional relationship according to the corresponding admittance of the battery at different test temperatures when the battery is offline;
  • the first determining module 902 is configured to obtain a temperature distribution model of the battery according to the shape and size of the battery, and determine the second functional relationship corresponding to the internal temperature and the surface temperature in combination with the first functional relationship, and the second functional relationship is used to combine the battery surface temperature and The battery admittance estimates the internal temperature of the battery.
  • the fitting module 901 includes:
  • the first determining sub-module 9011 is used to determine the characteristic test frequency when the battery is offline.
  • the characteristic test frequency belongs to the target frequency range. In the target frequency range, the impedance of the battery does not change with the change of the state of charge of the battery;
  • the second determining sub-module 9012 is used to determine the corresponding admittance of the battery at different test temperatures according to the characteristic test frequency;
  • the fitting sub-module 9013 is used to fit the first functional relationship according to different test temperatures and corresponding admittances.
  • the apparatus 900 further includes:
  • the detection module 903 is used to detect the target surface temperature of the battery to be estimated, and to detect the target admittance of the battery to be estimated;
  • the second determining module 904 is configured to determine the internal temperature of the battery to be estimated according to the target surface temperature and the target admittance in combination with the second functional relationship.
  • the first determining submodule 9011 is specifically configured to:
  • the target frequency range is determined from the preset frequency range, and at different temperatures, combined with the set charge State test the response value of the electrochemical impedance spectrum of the battery, convert the response value into the corresponding admittance, and determine the frequency at which the real part of the admittance changes the most with temperature in the target frequency range as the characteristic test frequency.
  • the fitting submodule 9013 is specifically used for:
  • the first determining module 902 is specifically configured to:
  • the shape of the battery is square.
  • the first functional relationship is fitted according to the corresponding admittance of the battery at different test temperatures, and the temperature distribution model of the battery is obtained according to the shape and size of the battery, combined with the first functional relationship Determine the second functional relationship corresponding to the internal temperature and the surface temperature.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can be combined with the real-time measured admittance and surface temperature of the battery.
  • the internal temperature is estimated, which effectively improves the accuracy of the internal temperature estimation of the battery.
  • FIG. 11 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • the electronic device 1000 includes a memory 1001, a processor 1002, and a computer program stored on the memory 1001 and running on the processor 1002.
  • the processor 1002 executes the program, the battery temperature estimation method in the foregoing embodiment is implemented.
  • the electronic device further includes a communication interface 1003 for communication between the memory 1001 and the processor 1002.
  • the first functional relationship is fitted according to the corresponding admittance of the battery at different test temperatures, and the temperature distribution model of the battery is obtained according to the shape and size of the battery, combined with the first functional relationship Determine the second functional relationship corresponding to the internal temperature and the surface temperature.
  • the second functional relationship is used to estimate the internal temperature of the battery in combination with the battery surface temperature and the battery admittance, which can be combined with the real-time measured admittance and surface temperature of the battery.
  • the internal temperature is estimated, which effectively improves the accuracy of the internal temperature estimation of the battery.
  • This embodiment also provides a computer-readable storage medium on which a computer program is stored, where the program is executed by a processor to implement the above battery temperature estimation method.
  • each part of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof.
  • multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if it is implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: Discrete logic circuits, application-specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • a person of ordinary skill in the art can understand that all or part of the steps carried in the method of the foregoing embodiments can be implemented by a program instructing relevant hardware to complete.
  • the program can be stored in a computer-readable storage medium. When executed, it includes one of the steps of the method embodiment or a combination thereof.
  • the functional units in the various embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.
  • the aforementioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

Abstract

L'invention concerne un procédé et un appareil d'estimation de la température d'une batterie, un dispositif électronique et un support de stockage, se rapportant au domaine technique des batteries. Le procédé consiste : lorsqu'une batterie est dans un état hors ligne, à ajuster une première relation fonctionnelle en fonction d'admittances correspondantes de la batterie à différentes températures de test ; et à obtenir un modèle de distribution de température de la batterie en fonction de la forme et de la taille de cette dernière, et à déterminer une seconde relation fonctionnelle correspondant à la température interne et à la température de surface par la combinaison de la première relation fonctionnelle, la seconde relation fonctionnelle étant utilisée pour estimer la température interne de la batterie par la combinaison de la température de surface de la batterie et de l'admittance de cette dernière.
PCT/CN2020/117853 2019-09-29 2020-09-25 Procédé et appareil d'estimation de température de batterie, dispositif électronique et support de stockage WO2021057917A1 (fr)

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JP2022519442A JP7379679B2 (ja) 2019-09-29 2020-09-25 電池温度推定方法、装置、電子機器、及び記憶媒体
EP20870190.4A EP4024069A4 (fr) 2019-09-29 2020-09-25 Procédé et appareil d'estimation de température de batterie, dispositif électronique et support de stockage
US17/764,340 US11846675B2 (en) 2019-09-29 2020-09-25 Battery temperature estimation method and apparatus, electronic device, and storage medium
KR1020227011623A KR20220057614A (ko) 2019-09-29 2020-09-25 배터리 온도를 추정하는 방법 및 장치, 전자 디바이스, 및 저장 매체

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Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112994585B (zh) * 2021-04-01 2023-07-21 安徽江淮汽车集团股份有限公司 电机的控制方法、电机和可读存储介质
CN114184964B (zh) * 2021-12-08 2023-08-29 蜂巢能源科技(无锡)有限公司 一种电芯内部温度分布的评估方法及装置
CN113945302B (zh) * 2021-12-20 2022-03-08 中创新航科技股份有限公司 一种电池内部温度的确定方法及其确定装置
CN114720890B (zh) * 2022-03-23 2023-04-21 四川大学 一种基于宽频阻抗谱的锂电池内部温度估计方法
CN116429280A (zh) * 2023-06-13 2023-07-14 广汽埃安新能源汽车股份有限公司 一种动力电池整包温度确定方法及装置

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160103184A1 (en) * 2014-10-09 2016-04-14 Denso Corporation Battery state estimation apparatus
CN105510843A (zh) * 2016-01-08 2016-04-20 北京北交新能科技有限公司 一种锂离子电池的低温频域电-热模型的构建方法
CN106289566A (zh) * 2016-07-19 2017-01-04 清华大学 一种基于电化学阻抗对二次电池内部温度估算的方法
CN106842050A (zh) * 2017-01-24 2017-06-13 中国电力科学研究院 一种电池温度预测方法及装置
CN107192952A (zh) * 2017-03-31 2017-09-22 中国电力科学研究院 一种电池内部温度检测方法和装置
JP2018037332A (ja) * 2016-09-01 2018-03-08 トヨタ自動車株式会社 電池温度の推定方法
CN109425833A (zh) * 2017-08-24 2019-03-05 丰田自动车株式会社 温度推定装置

Family Cites Families (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6137269A (en) * 1999-09-01 2000-10-24 Champlin; Keith S. Method and apparatus for electronically evaluating the internal temperature of an electrochemical cell or battery
CN100440614C (zh) 2007-01-26 2008-12-03 清华大学 一种实时估计镍氢动力电池内外温差的方法
JP2010135075A (ja) * 2008-12-02 2010-06-17 Calsonic Kansei Corp 組電池の温度推定方法及び装置
JP2010243481A (ja) * 2009-03-18 2010-10-28 National Institute Of Advanced Industrial Science & Technology 二次電池の温度に関する状態を判定する方法、判定装置および判定プログラム
CN102576914B (zh) * 2009-10-14 2014-10-29 株式会社日立制作所 电池控制装置以及电动机驱动系统
JP2013118724A (ja) * 2011-12-01 2013-06-13 Toyota Motor Corp 制御装置および制御方法
US10230137B2 (en) * 2012-05-23 2019-03-12 The Regents Of The University Of Michigan Estimating core temperatures of battery cells in a battery pack
US9880061B2 (en) 2013-06-14 2018-01-30 Hrl Laboratories, Llc Methods and apparatus for sensing the internal temperature of an electrochemical device
JP6200359B2 (ja) * 2014-03-20 2017-09-20 古河電気工業株式会社 二次電池内部温度推定装置および二次電池内部温度推定方法
JP5768160B1 (ja) * 2014-04-16 2015-08-26 エスペック株式会社 二次電池容量評価方法および評価装置
JP2016167420A (ja) * 2015-03-10 2016-09-15 富士重工業株式会社 車載二次電池の温度制御装置
CN104865534B (zh) 2015-04-29 2017-12-26 同济大学 一种单体电池内部温度估计方法
CN104882643A (zh) * 2015-05-18 2015-09-02 北京波士顿动力电池有限公司 一种表征硬包装类电池内部温度稳定性和动态变化的方法
CN107219021B (zh) * 2016-03-22 2020-10-16 罗伯特·博世有限公司 一种用于检测电池温度的方法和装置
CN106772075B (zh) * 2016-12-09 2019-07-05 同济大学 一种考虑温度梯度的在线电池阻抗模型优化方法
JP6760119B2 (ja) * 2017-02-06 2020-09-23 住友電気工業株式会社 電池温度推定装置、電池温度推定方法及びコンピュータプログラム
CN106872904B (zh) 2017-02-23 2019-02-26 合肥工业大学 基于离散滑模观测器的锂电池内部温度估计方法
JP6826935B2 (ja) * 2017-03-30 2021-02-10 古河電気工業株式会社 二次電池内部温度推定装置および二次電池内部温度推定方法
US10554069B2 (en) * 2017-12-15 2020-02-04 Medtronic, Inc. Medical device temperature estimation
CN109799005A (zh) 2018-12-20 2019-05-24 北京长城华冠汽车科技股份有限公司 电池内部温度估计方法、装置、系统和车辆
CN109921111B (zh) 2019-03-14 2020-08-04 上海大学 一种锂离子电池内部温度估测方法及系统

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160103184A1 (en) * 2014-10-09 2016-04-14 Denso Corporation Battery state estimation apparatus
CN105510843A (zh) * 2016-01-08 2016-04-20 北京北交新能科技有限公司 一种锂离子电池的低温频域电-热模型的构建方法
CN106289566A (zh) * 2016-07-19 2017-01-04 清华大学 一种基于电化学阻抗对二次电池内部温度估算的方法
JP2018037332A (ja) * 2016-09-01 2018-03-08 トヨタ自動車株式会社 電池温度の推定方法
CN106842050A (zh) * 2017-01-24 2017-06-13 中国电力科学研究院 一种电池温度预测方法及装置
CN107192952A (zh) * 2017-03-31 2017-09-22 中国电力科学研究院 一种电池内部温度检测方法和装置
CN109425833A (zh) * 2017-08-24 2019-03-05 丰田自动车株式会社 温度推定装置

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US20220357399A1 (en) 2022-11-10
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